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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkGaussianSpatialFunction.txx,v $
Language: C++
Date: $Date: 2007-02-13 15:53:33 $
Version: $Revision: 1.11 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkGaussianSpatialFunction_txx
#define __itkGaussianSpatialFunction_txx
#include <math.h>
#include "itkGaussianSpatialFunction.h"
namespace itk
{
template <typename TOutput, unsigned int VImageDimension, typename TInput>
GaussianSpatialFunction<TOutput, VImageDimension, TInput>
::GaussianSpatialFunction()
{
m_Mean = ArrayType::Filled(10.0);
m_Sigma = ArrayType::Filled(5.0);
m_Scale = 1.0;
m_Normalized = false;
}
template <typename TOutput, unsigned int VImageDimension, typename TInput>
GaussianSpatialFunction<TOutput, VImageDimension, TInput>
::~GaussianSpatialFunction()
{
}
template <typename TOutput, unsigned int VImageDimension, typename TInput>
typename GaussianSpatialFunction<TOutput, VImageDimension, TInput>::OutputType
GaussianSpatialFunction<TOutput, VImageDimension, TInput>
::Evaluate(const TInput& position) const
{
// We have to compute the gaussian in several stages, because of the
// n-dimensional generalization
// Normalizing the Gaussian is important for statistical applications
// but is generally not desirable for creating images because of the
// very small numbers involved (would need to use doubles)
double prefixDenom;
if (m_Normalized)
{
prefixDenom = m_Sigma[0];
for(unsigned int i = 1; i < VImageDimension; i++)
{
prefixDenom *= m_Sigma[i];
}
prefixDenom *= 2 * 3.1415927;
}
else
{
prefixDenom = 1.0;
}
double suffixExp = 0;
for(unsigned int i = 0; i < VImageDimension; i++)
{
suffixExp += (position[i] - m_Mean[i])*(position[i] - m_Mean[i])
/ (2 * m_Sigma[i] * m_Sigma[i]);
}
double value = m_Scale * (1 / prefixDenom) * vcl_exp(-1 * suffixExp);
return (TOutput) value;
}
template <typename TOutput, unsigned int VImageDimension, typename TInput>
void
GaussianSpatialFunction<TOutput, VImageDimension, TInput>
::PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os,indent);
unsigned int i;
os << indent << "Sigma: [";
for (i=0; i+1 < VImageDimension ; i++)
{
os << m_Sigma[i] << ", ";
}
os << "]" << std::endl;
os << indent << "Mean: [";
for (i=0; i+1 < VImageDimension ; i++)
{
os << m_Mean[i] << ", ";
}
os << "]" << std::endl;
os << indent << "Scale: " << m_Scale << std::endl;
os << indent << "Normalized?: " << m_Normalized << std::endl;
}
} // end namespace itk
#endif
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